Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence
Abstract Background In the past two decades, biologists have been able to identify the gene signatures associated with various phenotypes through the monitoring of gene expressions with high-throughput biotechnologies. These gene signatures have in turn been successfully applied to drug development,...
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doaj-97c8e1d26b074f918a696ed073dff3f72020-11-25T03:05:32ZengSpringerOpenBotanical Studies1999-31102019-09-0160111610.1186/s40529-019-0268-8Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependenceLi-yu Daisy Liu0Ya-Chun Hsiao1Hung-Chi Chen2Yun-Wei Yang3Men-Chi Chang4Department of Agronomy, National Taiwan UniversityDepartment of Agronomy, National Taiwan UniversityDepartment of Horticulture and Landscape Architecture, National Taiwan UniversityDepartment of Agronomy, National Taiwan UniversityDepartment of Agronomy, National Taiwan UniversityAbstract Background In the past two decades, biologists have been able to identify the gene signatures associated with various phenotypes through the monitoring of gene expressions with high-throughput biotechnologies. These gene signatures have in turn been successfully applied to drug development, disease prevention, crop improvement, etc. However, ignoring the interactions among genes has weakened the predictive power of gene signatures in practical applications. Gene regulatory networks, in which genes are represented by nodes and the associations between genes are represented by edges, are typically constructed to analyze and visualize such gene interactions. More specifically, the present study sought to measure gene–gene associations by using the coefficient of intrinsic dependence (CID) to capture more nonlinear as well as cause-effect gene relationships. Results A stepwise procedure using the CID along with the partial coefficient of intrinsic dependence (pCID) was demonstrated for the rebuilding of simulated networks and the well-known CBF-COR pathway under cold stress using Arabidopsis microarray data. The procedure was also applied to the construction of bHLH gene regulatory pathways under abiotic stresses using rice microarray data, in which OsbHLH104, a putative phytochrome-interacting factor (OsPIF14), and OsbHLH060, a positive regulator of iron homeostasis (OsPRI1) were inferred as the most affiliated genes. The inferred regulatory pathways were verified through literature reviews. Conclusions The proposed method can efficiently decipher gene regulatory pathways and may assist in achieving higher predictive power in practical applications. The lack of any mention in the literature of some of the regulatory event may have been due to the high complexity of the regulatory systems in the plant transcription, a possibility which could potentially be confirmed in the near future given ongoing rapid developments in bio-technology.http://link.springer.com/article/10.1186/s40529-019-0268-8Gene regulatory networkCause-effect relationshipMicroarrayCoefficient of intrinsic dependence |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Li-yu Daisy Liu Ya-Chun Hsiao Hung-Chi Chen Yun-Wei Yang Men-Chi Chang |
spellingShingle |
Li-yu Daisy Liu Ya-Chun Hsiao Hung-Chi Chen Yun-Wei Yang Men-Chi Chang Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence Botanical Studies Gene regulatory network Cause-effect relationship Microarray Coefficient of intrinsic dependence |
author_facet |
Li-yu Daisy Liu Ya-Chun Hsiao Hung-Chi Chen Yun-Wei Yang Men-Chi Chang |
author_sort |
Li-yu Daisy Liu |
title |
Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence |
title_short |
Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence |
title_full |
Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence |
title_fullStr |
Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence |
title_full_unstemmed |
Construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence |
title_sort |
construction of gene causal regulatory networks using microarray data with the coefficient of intrinsic dependence |
publisher |
SpringerOpen |
series |
Botanical Studies |
issn |
1999-3110 |
publishDate |
2019-09-01 |
description |
Abstract Background In the past two decades, biologists have been able to identify the gene signatures associated with various phenotypes through the monitoring of gene expressions with high-throughput biotechnologies. These gene signatures have in turn been successfully applied to drug development, disease prevention, crop improvement, etc. However, ignoring the interactions among genes has weakened the predictive power of gene signatures in practical applications. Gene regulatory networks, in which genes are represented by nodes and the associations between genes are represented by edges, are typically constructed to analyze and visualize such gene interactions. More specifically, the present study sought to measure gene–gene associations by using the coefficient of intrinsic dependence (CID) to capture more nonlinear as well as cause-effect gene relationships. Results A stepwise procedure using the CID along with the partial coefficient of intrinsic dependence (pCID) was demonstrated for the rebuilding of simulated networks and the well-known CBF-COR pathway under cold stress using Arabidopsis microarray data. The procedure was also applied to the construction of bHLH gene regulatory pathways under abiotic stresses using rice microarray data, in which OsbHLH104, a putative phytochrome-interacting factor (OsPIF14), and OsbHLH060, a positive regulator of iron homeostasis (OsPRI1) were inferred as the most affiliated genes. The inferred regulatory pathways were verified through literature reviews. Conclusions The proposed method can efficiently decipher gene regulatory pathways and may assist in achieving higher predictive power in practical applications. The lack of any mention in the literature of some of the regulatory event may have been due to the high complexity of the regulatory systems in the plant transcription, a possibility which could potentially be confirmed in the near future given ongoing rapid developments in bio-technology. |
topic |
Gene regulatory network Cause-effect relationship Microarray Coefficient of intrinsic dependence |
url |
http://link.springer.com/article/10.1186/s40529-019-0268-8 |
work_keys_str_mv |
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